Semantic Interoperability Architecture for Pervasive Computing and Internet of Things

Pervasive computing and Internet of Things (IoTs) paradigms have created a huge potential for new business. To fully realize this potential, there is a need for a common way to abstract the heterogeneity of devices so that their functionality can be represented as a virtual computing platform. To this end, we present novel semantic level interoperability architecture for pervasive computing and IoTs. There are two main principles in the proposed architecture. First, information and capabilities of devices are represented with semantic web knowledge representation technologies and interaction with devices and the physical world is achieved by accessing and modifying their virtual representations. Second, global IoT is divided into numerous local smart spaces managed by a semantic information broker (SIB) that provides a means to monitor and update the virtual representation of the physical world. An integral part of the architecture is a resolution infrastructure that provides a means to resolve the network address of a SIB either using a physical object identifier as a pointer to information or by searching SIBs matching a specification represented with SPARQL. We present several reference implementations and applications that we have developed to evaluate the architecture in practice. The evaluation also includes performance studies that, together with the applications, demonstrate the suitability of the architecture to real-life IoT scenarios. In addition, to validate that the proposed architecture conforms to the common IoT-A architecture reference model (ARM), we map the central components of the architecture to the IoT-ARM

Architecture for mixed criticality resource management in Internet of Things

We believe that the next big step in the field of Internet of Things (IoT) is to realize a virtual computing platform that provides access to heterogeneous group of device resources present in our living environments. By enabling 3rd party developers to access sensor and actuator resources present in a given environment in a same way they can access resources of a single mobile phone, the virtual computing platform would open a new market for the 3rd party IoT applications like the smart phones have done for mobile apps. To accomplish this vision, the virtual computing platform must be able to manage resource sharing between applications with differing criticality requirements for ensuring that the whole IoT system runs optimally. The main challenge is that the approach should be generic and extendable for future needs. To tackle this issue, we propose a two-level resource management architecture, where the necessary information about applications and resources are represented with machine-interpretable semantic descriptions based on the Semantic Web technologies. At the system level, these descriptions are used by the global resource manager for allocating resources to the applications based on their criticality and needs. At local level, each device is assigned with a local resource manager that schedules the access to resources provided by the device so that the performance of the more critical applications could be optimized at the expense of the less critical ones. To evaluate our approach in practice, we have implemented a reference implementation of the proposed architecture and demonstrated it through several applications with differing criticality levels. The results are very promising for managing mixed criticality applications in IoT

SIC-EDGE: Semantic Iterative ECG Compression for Edge-Assisted Wearable Systems

Wearable sensors and Internet of Things technologies are enabling automated health monitoring applications, where signals captured by sensors are analyzed in real-time by algorithms detecting health issues and conditions. However, continuous clinical-level monitoring of patients in everyday settings often requires computation, storage and connectivity capabilities beyond those possessed by wearable sensors. While edge computing partially resolves this issue by connecting the sensors to compute-capable devices positioned at the network edge, the wireless links connecting the sensors to the edge servers may not have sufficient capacity to transfer the information-rich data that characterize these applications. A possible solution is to compress the signal to be transferred, accepting the tradeoff between compression gain and detection accuracy. In this paper, we propose SIC-EDGE: a "semantic compression"framework whose goal is to dynamically optimize the resolution of an electrocardiogram (ECG) signal transferred from a wearable sensor to an edge server to perform real-time detection of heart diseases. The core idea is to establish a collaborative control loop between the sensor and the edge server to iteratively build a semantic representation that is: (i) ECG-cycle specific; (ii) personalized, and (iii) targeted to support the classification task rather than signal reconstruction. The core of SIC-EDGE is a Sequential Hypothesis Testing (SHT) algorithm that analyzes partial representations along the iterations to determine which and how many representation layers (wavelet coefficients in our implementation) are requested. Our results on established datasets demonstrates the need for adaptive "semantic"compression, and illustrate the dynamic compression strategy realized by SIC-EDGE. We show that SIC-EDGE leads to an increase in terms of recall and F1 score of up to 35% and 26% respectively compared to an optimized but static wavelet compression for a given maximum channel usage